The ability to act quickly and decisively in today's increasingly competitive marketplace is critical to the success of any organization. The volume of data that is available to organizations is rapidly increasing and frequently overwhelming. The availability of large volumes of data presents various challenges. One challenge is to avoid inundating an individual with unnecessary information. Another challenge is to ensure all relevant information is available in a timely manner.
One known approach to addressing these and other challenges is known as data warehousing. Data warehouses, relational databases, and data marts are becoming important elements of many information delivery systems because they provide a central location where a reconciled version of data extracted from a wide variety of operational systems may be stored. As used herein, a data warehouse should be understood to be an informational database that stores shareable data from one or more operational databases of record, such as one or more transaction-based database systems. A data warehouse typically allows users to tap into a business's vast store of operational data to track and respond to business trends that facilitate forecasting and planning efforts. A data mart may be considered to be a type of data warehouse that focuses on a particular business segment.
Decision support systems have been developed to efficiently retrieve selected information from data warehouses. One type of decision support system is known as an on-line analytical processing system (“OLAP”). In general, OLAP systems analyze the data from a number of different perspectives and support complex analyses against large input data sets.
There are at least three different types of OLAP architectures—ROLAP, MOLAP, and HOLAP. ROLAP (“Relational On-Line Analytical Processing”) systems are systems that use a dynamic server connected to a relational database system. Multidimensional OLAP (“MOLAP”) utilizes a proprietary multidimensional database (“MDDB”) to provide OLAP analyses. The main premise of this architecture is that data must be stored multidimensionally to be viewed multidimensionally. A HOLAP (“Hybrid On-Line Analytical Processing”) system is a hybrid of these two.
ROLAP is a three-tier, client/server architecture comprising a presentation tier, an application logic tier and a relational database tier. The relational database tier stores data and connects to the application logic tier. The application logic tier comprises a ROLAP engine that executes multidimensional reports from multiple end users. The ROLAP engine integrates with a variety of presentation layers, through which users perform OLAP analyses. The presentation layers enable users to provide requests to the ROLAP engine. The premise of ROLAP is that OLAP capabilities are best provided directly against a relational database, e.g., the data warehouse.
In a ROLAP system, data from transaction-processing systems is loaded into a defined data model in the data warehouse. Database routines are run to aggregate the data, if required by the data model. Indices are then created to optimize query access times. End users submit multidimensional analyses to the ROLAP engine, which then dynamically transforms the requests into SQL execution plans. The SQL is submitted to the relational database for processing, the relational query results are cross-tabulated, and a multidimensional result set is returned to the end user. ROLAP is a fully dynamic architecture capable of utilizing pre-calculated results when they are available, or dynamically generating results from atomic information when necessary.
The ROLAP architecture directly accesses data from data warehouses, and therefore supports optimization techniques to meet batch window requirements and to provide fast response times. These optimization techniques typically include application-level table partitioning, aggregate inferencing, denormalization support, and multiple fact table joins.
MOLAP is a two-tier, client/server architecture. In this architecture, the MDDB serves as both the database layer and the application logic layer. In the database layer, the MDDB system is responsible for all data storage, access, and retrieval processes. In the application logic layer, the MDDB is responsible for the execution of all OLAP requests. The presentation layer integrates with the application logic layer and provides an interface through which the end users view and request OLAP analyses. The client/server architecture allows multiple users to access the multidimensional database.
Information from a variety of transaction-processing systems is loaded into the MDDB System through a series of batch routines. Once this atomic data has been loaded into the MDDB, the general approach is to perform a series of batch calculations to aggregate along the orthogonal dimensions and fill the MDDB array structures. For example, revenue figures for all of the stores in a state would be added together to fill the state level cells in the database. After the array structure in the database has been filled, indices are created and hashing algorithms are used to improve query access times.
Once this compilation process has been completed, the MDDB is ready for use. Users request OLAP reports through the presentation layer, and the application logic layer of the MDDB retrieves the stored data.
The MOLAP architecture is a compilation-intensive architecture. It principally reads the pre-compiled data, and has limited capabilities to dynamically create aggregations or to calculate business metrics that have not been pre-calculated and stored.
The hybrid OLAP (“HOLAP”) solution is a mix of MOLAP and relational architectures that support inquiries against summary and transaction data in an integrated fashion. The HOLAP approach enables a user to perform multidimensional analysis on data in the MDDB. However, if the user reaches the bottom of the multidimensional hierarchy and requires more detailed data, the HOLAP engine generates an SQL to retrieve the detailed data from the source relational database management system (“RDBMS”) and returns it to the end user. HOLAP implementations rely on simple SQL statements to pull large quantities of data into the mid-tier, multidimensional engine for processing. This constrains the range of inquiry and returns large, unrefined result sets that can overwhelm networks with limited bandwidth.
As described above, each of these types of OLAP systems are typically client-server systems. The OLAP engine resides on the server side and a module is typically provided at a client-side to enable users to input queries and report requests to the OLAP engine. Current client-side modules are typically stand alone software modules that are loaded on client-side computer systems. One drawback of such systems is that a user must learn how to operate the client-side software module in order to initiate queries and generate reports.
Although various user interfaces have been developed to enable users to access the content of data warehouses through server systems, many such systems experience significant drawbacks. All of these systems require that the user connect via a computer system to the server system to initiate reports and view the contents of the reports.
Moreover, current systems require that the user initiate a request for a report each time the user desires to have that report generated. A particular user may desire to run a particular report frequently to determine the status of the report.
Further, reports may be extensive and may contain a large amount of information for a user to sort through each time a report is run. A particular user may only be interested in knowing if a particular value or set of values in the report has changed over a predetermined period of time. Current systems require the user to initiate the new report and then scan through the new report to determine if the information has changed over the time period specified.
These and other drawbacks exist with current OLAP interface systems.